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COPD Classification in CT Images Using a 3D Convolutional Neural Network

  • Jalil AhmedEmail author
  • Sulaiman Vesal
  • Felix Durlak
  • Rainer Kaergel
  • Nishant Ravikumar
  • Martine Rémy-Jardin
  • Andreas Maier
Conference paper
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Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a diffcult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing methods that can automatically classify COPD versus healthy patients is of great interest. In this paper, we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Jalil Ahmed
    • 1
    Email author
  • Sulaiman Vesal
    • 1
  • Felix Durlak
    • 2
  • Rainer Kaergel
    • 2
  • Nishant Ravikumar
    • 1
    • 3
  • Martine Rémy-Jardin
    • 4
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangen-NürnbergDeutschland
  2. 2.Siemens Healthcare GmbHForchheimDeutschland
  3. 3.CISTIB, Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of ComputingUniversity of LeedsLeedsUnited Kingdom
  4. 4.Département d’Imagerie ThoraciqueCHRU LilleLilleFrankreich

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